Over the past several years the development of face recognition systems has been receiving increased attention as having the potential for providing a non-invasive way of improving security systems and homeland defense. Such systems may be used for applications such as access control to restricted areas, by either control of physical entry into a building, room, vault or an outdoor area or electronically such as to a computer system or ATM. Another application of such systems is identification of individuals on a known watchlist, which can consist of but is not limited to, known criminals, terrorists, or casino cardcounters. For identification a face recognition system produces a rank ordering of known individuals that closely match an unknown subject. If there is an identification matched ranking of N (e.g., N=10) or less with a known malevolent individual, then the unknown subject can either be detained or taken to a secondary procedure where further information is solicited. Another set of applications include surveillance and monitoring of scenes whereby the identity of individuals present in a scene is periodically verified.
Existing end-to-end systems that detect and recognize faces of individuals at a distance are exclusively performed with visible light video cameras. The influence of varying ambient illumination on systems using visible imagery is well-known to be one of the major limiting factors for recognition performance [Wilder, Joseph and Phillips, P. Jonathon and Jiang, Cunhong and Wiener, Stephen, “Comparison of Visible and Infra-Red Imagery for Face Recognition,” Proceedings of 2nd International Conference on Automatic Face & Gesture Recognition, pp. 182-187, Killington, Vt., 1996; Adini, Yael and Moses, Yael and Ullman, Shimon, “Face Recognition: The Problem of Compensating for Changes in Illumination Direction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 19, No. 7, pp. 721-732, July, 1997]. A variety of methods compensating for variations in illumination have been studied in order to boost recognition performance, including histogram equalization, Laplacian transforms, Gabor transforms, logarithmic transforms, and 3-D shape-based methods. These techniques aim at reducing the within-class variability introduced by changes in illumination, which has been shown to be often larger than the between-class variability in the data, thus severely affecting classification performance. System performance, particularly outdoors where illumination is dynamic, is problematic with existing systems.
Face recognition in the thermal infrared domain has received relatively little attention compared with recognition systems using visible-spectrum imagery. Original tentative analyses have focused mostly on validating the thermal imagery of faces as a valid biometric [Prokoski, F. J., “History, Current Status, and Future of Infrared Identification, Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, Hilton Head, June, 2000; Wilder, Joseph and Phillips, P. Jonathon and Jiang, Cunhong and Wiener, Stephen, “Comparison of Visible and Infra-Red Imagery for Face Recognition,” Proceedings of 2nd International Conference on Automatic Face & Gesture Recognition, pp. 182-187, Killington, Vt., 1996]. The lower interest level in infrared imagery has been based in part on the following factors: much higher cost of thermal sensors versus visible video equipment, lower image resolution, higher image noise, and lack of widely available data sets. These historical objections are becoming less relevant as infrared imaging technology advances, making it attractive to consider thermal sensors in the context of face recognition.